code: def __init__(
      self,
      learning_rate: float = 0.001,
      weight_decay: float = 0.0,
      auxiliary_loss_weight: float = 0.4,
      max_epochs: int = 600,
  ):
      self.auxiliary_loss_weight = auxiliary_loss_weight
      # Training length will be used in LR scheduler
      self.max_epochs = max_epochs
      super().__init__(
          learning_rate=learning_rate,
          weight_decay=weight_decay,
          export_onnx=False,
      )
  
imports: import nni
  import torch
  from torchvision import transforms
  from torchvision.datasets import CIFAR10
  from nni.nas.evaluator.pytorch import DataLoader
  from nni.nas.hub.pytorch import DARTS
  import numpy
  from nni.nas.evaluator.pytorch import Classification
  from torch.utils.data import SubsetRandomSampler
  from nni.nas.strategy import DARTS
  from nni.nas.experiment import NasExperiment
  import io
  import graphviz
  import matplotlib.pyplot
  from PIL import Image
  from nni.nas.space import model_context
  import torch
  from nni.nas.evaluator.pytorch import ClassificationModule
  from nni.nas.evaluator.pytorch import Lightning, Trainer
label_desc: Use image node_id 1 to create a new DartsClassificationModule object from inherited base classes: ClassificationModule with example: obj = DartsClassificationModule(learning_rate, weight_decay, auxiliary_loss_weight, max_epochs)